def test_FinInterpolatedForwards():
import matplotlib.pyplot as plt
tValues = np.array([0.0, 3.0, 5.0, 10.0])
rValues = np.array([0.04, 0.07, 0.08, 0.09])
dfValues = np.exp(-tValues*rValues)
tInterpValues = np.linspace(0.0, 12.0, 200)
print(tValues)
print(rValues)
print(dfValues)
curveDate = FinDate(2019,1,3)
for method in FinInterpMethods:
discountCurve = FinDiscountCurve(curveDate, tValues, dfValues, method)
dfInterpValues = discountCurve.df(tInterpValues)
fwdInterpValues = discountCurve.fwd(tInterpValues)
zeroInterpValues = discountCurve.zeroRate(tInterpValues)
if PLOT_GRAPHS:
plt.figure(figsize=(8, 6))
plt.plot(tValues, dfValues, 'o', color='g', label="DFS:")
plt.plot(tInterpValues, dfInterpValues, color='r', label="DF:" + str(method))
plt.legend()
plt.figure(figsize=(8, 6))
plt.plot(tInterpValues, fwdInterpValues, color='r', label="FWD:" + str(method))
plt.plot(tInterpValues, zeroInterpValues, color='b', label="ZERO:" + str(method))
plt.plot(tValues, rValues, 'o', color='g', label="ZERO RATES")
plt.legend()
def test_FinDiscountCurve():
startDate = FinDate(2018, 1, 1)
times = np.linspace(0, 10.0, 10)
rate = 0.05
values = np.exp(-rate * times)
curve = FinDiscountCurve(startDate, times, values,
FinInterpMethods.FLAT_FORWARDS)
testCases.header("T", "DF")
for t in np.linspace(0, 10, 21):
df = curve.df(t)
testCases.print(t, df)
def test_CDSFastApproximation():
valueDate = FinDate(2018, 6, 20)
# I build a discount curve that requires no bootstrap
times = np.linspace(0, 10.0, 11)
r = 0.05
discountFactors = np.power((1.0 + r), -times)
dates = valueDate.addYears(times)
liborCurve = FinDiscountCurve(valueDate,
dates,
discountFactors,
FinInterpTypes.FLAT_FWD_RATES)
##########################################################################
maturityDate = valueDate.nextCDSDate(120)
t = (maturityDate - valueDate) / 365.242
z = liborCurve.df(maturityDate)
r = -np.log(z) / t
recoveryRate = 0.40
contractCoupon = 0.010
testCases.header("MKT_SPD", "EXACT_VALUE", "APPROX_VALUE", "DIFF(%NOT)")
for mktCoupon in np.linspace(0.000, 0.05, 21):
cdsContracts = []
cdsMkt = FinCDS(valueDate, maturityDate, mktCoupon, ONE_MILLION)
cdsContracts.append(cdsMkt)
issuerCurve = FinCDSCurve(valueDate,
cdsContracts,
liborCurve,
recoveryRate)
cdsContract = FinCDS(valueDate, maturityDate, contractCoupon)
v_exact = cdsContract.value(
valueDate, issuerCurve, recoveryRate)['full_pv']
v_approx = cdsContract.valueFastApprox(
valueDate, r, mktCoupon, recoveryRate)[0]
pctdiff = (v_exact - v_approx) / ONE_MILLION * 100.0
testCases.print(mktCoupon * 10000, v_exact, v_approx, pctdiff)
def test_FinInterpolatedForwards():
import matplotlib.pyplot as plt
tValues = np.array([0.0, 3.0, 5.0, 10.0])
rValues = np.array([0.04, 0.07, 0.08, 0.09])
dfValues = np.exp(-tValues * rValues)
tInterpValues = np.linspace(0.0, 12.0, 49)
curveDate = FinDate(1, 1, 2019)
tDates = curveDate.addYears(tValues)
tInterpDates = curveDate.addYears(tInterpValues)
for interpType in FinInterpTypes:
discountCurve = FinDiscountCurve(curveDate, tDates, dfValues,
interpType)
dfInterpValues = discountCurve.df(tInterpDates)
fwdInterpValues = discountCurve.fwd(tInterpDates)
zeroInterpValues = discountCurve.zeroRate(tInterpDates)
if PLOT_GRAPHS:
plt.figure(figsize=(8, 6))
plt.plot(tValues, dfValues, 'o', color='g', label="DFS:")
plt.plot(tInterpValues,
dfInterpValues,
color='r',
label="DF:" + str(interpType))
plt.legend()
plt.figure(figsize=(8, 6))
plt.plot(tInterpValues,
fwdInterpValues,
color='r',
label="FWD:" + str(interpType))
plt.plot(tInterpValues,
zeroInterpValues,
color='b',
label="ZERO:" + str(interpType))
plt.plot(tValues, rValues, 'o', color='g', label="ZERO RATES")
plt.legend()